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      "cell_type": "markdown",
      "metadata": {
        "id": "ClOqe_6GHB_3",
        "colab_type": "text"
      },
      "source": [
        "##### Copyright 2020 Google LLC.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHsFiv-UHFBq",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "\u003ca href=\"https://colab.research.google.com/github/google-research/big_transfer/blob/master/colabs/big_transfer_tf2.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXY0spPLdxkX",
        "colab_type": "text"
      },
      "source": [
        "# BigTransfer (BiT): A step-by-step tutorial for state-of-the-art vision\n",
        "\n",
        "By Jessica Yung and Joan Puigcerver\n",
        "\n",
        "In this colab, we will show you how to load one of our BiT models (a ResNet50 trained on ImageNet-21k), use it out-of-the-box and fine-tune it on a dataset of flowers.\n",
        "\n",
        "This colab accompanies our [the TensorFlow blog post]() (TODO: link) and is based on the [BigTransfer paper](https://arxiv.org/abs/1912.11370).\n",
        "\n",
        "Our models can be [found on TensorFlow Hub here](https://tfhub.dev/google/collections/bit/1). We also share code to fine-tune our models in TensorFlow2, JAX and PyTorch in [our GitHub repository](https://github.com/google-research/big_transfer).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pitZxiw5hHYq",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Imports\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import time\n",
        "\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import os\n",
        "import pathlib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7i3p4Zmr0J0X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "cellView": "form",
        "outputId": "ca065e58-11df-4bb9-ff88-72fbdaab30d6"
      },
      "source": [
        "#@title Construct imagenet logit-to-class-name dictionary (imagenet_int_to_str)\n",
        "\n",
        "!wget https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt\n",
        "\n",
        "imagenet_int_to_str = {}\n",
        "\n",
        "with open('ilsvrc2012_wordnet_lemmas.txt', 'r') as f:\n",
        "  for i in range(1000):\n",
        "    row = f.readline()\n",
        "    row = row.rstrip()\n",
        "    imagenet_int_to_str.update({i: row})"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-19 12:32:01--  https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.217.128, 2607:f8b0:400c:c15::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.217.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 21675 (21K) [text/plain]\n",
            "Saving to: ‘ilsvrc2012_wordnet_lemmas.txt.1’\n",
            "\n",
            "\r          ilsvrc201   0%[                    ]       0  --.-KB/s               \rilsvrc2012_wordnet_ 100%[===================>]  21.17K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-05-19 12:32:01 (111 MB/s) - ‘ilsvrc2012_wordnet_lemmas.txt.1’ saved [21675/21675]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3DBgilDnevv",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title tf_flowers label names (hidden)\n",
        "tf_flowers_labels = ['dandelion', 'daisy', 'tulips', 'sunflowers', 'roses']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W97ueEAtk9k4",
        "colab_type": "text"
      },
      "source": [
        "# Load the pre-trained BiT model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jw8LOz3NgNG3",
        "colab_type": "text"
      },
      "source": [
        "First, we will load a pre-trained BiT model. There are ten models you can choose from, spanning two upstream training datasets and five ResNet architectures.\n",
        "\n",
        "In this tutorial, we will use a ResNet50x1 model trained on ImageNet-21k."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRd5i7iEIBz9",
        "colab_type": "text"
      },
      "source": [
        "### Where to find the models\n",
        "\n",
        "Models that output image features (pre-logit layer) can be found at\n",
        "* `https://tfhub.dev/google/bit/m-{archi, e.g. r50x1}/1`\n",
        "\n",
        "whereas models that return outputs in the Imagenet  (ILSVRC-2012) label space can be found at \n",
        "\n",
        "* `https://tfhub.dev/google/bit/m-{archi, e.g. r50x1}/ilsvrc2012_classification/1`\n",
        "\n",
        "The architectures we have include R50x1, R50x3, R101x1, R101x3 and R152x4. The architectures are all in lowercase in the links.\n",
        "\n",
        "So for example, if you want image features from a ResNet-50, you could use the model at `https://tfhub.dev/google/bit/m-r50x1/1`. This is also the model  we'll use in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1i8HCwx28Ya2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load model into KerasLayer\n",
        "model_url = \"https://tfhub.dev/google/bit/m-r50x1/1\"\n",
        "module = hub.KerasLayer(model_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxaRZvbTnUPI",
        "colab_type": "text"
      },
      "source": [
        "## Use BiT out-of-the-box\n",
        "\n",
        "If you don’t yet have labels for your images (or just want to have some fun), you may be interested in using the model out-of-the-box, i.e. without fine-tuning it. For this, we will use a model fine-tuned on ImageNet so it has the interpretable ImageNet label space of 1k classes. Many common objects are not covered, but it gives a reasonable idea of what is in the image. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hf9Rt79MxwKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load model fine-tuned on ImageNet\n",
        "model_url = \"https://tfhub.dev/google/bit/m-r50x1/ilsvrc2012_classification/1\"\n",
        "imagenet_module = hub.KerasLayer(model_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuAUqyWbx3Vn",
        "colab_type": "text"
      },
      "source": [
        "Using the model is very simple:\n",
        "```\n",
        "logits = module(image)\n",
        "```\n",
        "\n",
        "Note that the BiT models take inputs of shape [?, ?, 3] (i.e. 3 colour channels) with values between 0 and 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewOVUVsvnSpo",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Helper functions for loading image (hidden)\n",
        "\n",
        "def preprocess_image(image):\n",
        "  image = np.array(image)\n",
        "  # reshape into shape [batch_size, height, width, num_channels]\n",
        "  img_reshaped = tf.reshape(image, [1, image.shape[0], image.shape[1], image.shape[2]])\n",
        "  # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n",
        "  image = tf.image.convert_image_dtype(img_reshaped, tf.float32)  \n",
        "  return image\n",
        "\n",
        "def load_image_from_url(url):\n",
        "  \"\"\"Returns an image with shape [1, height, width, num_channels].\"\"\"\n",
        "  response = requests.get(url)\n",
        "  image = Image.open(BytesIO(response.content))\n",
        "  image = preprocess_image(image)\n",
        "  return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8fDpUyl75pT",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Plotting helper functions (hidden)\n",
        "#@markdown Credits to Xiaohua Zhai, Lucas Beyer and Alex Kolesnikov from Brain Zurich, Google Research\n",
        "\n",
        "# Show the MAX_PREDS highest scoring labels:\n",
        "MAX_PREDS = 5\n",
        "# Do not show labels with lower score than this:\n",
        "MIN_SCORE = 0.8 \n",
        "\n",
        "def show_preds(logits, image, correct_flowers_label=None, tf_flowers_logits=False):\n",
        "\n",
        "  if len(logits.shape) > 1:\n",
        "    logits = tf.reshape(logits, [-1])\n",
        "\n",
        "  fig, axes = plt.subplots(1, 2, figsize=(7, 4), squeeze=False)\n",
        "\n",
        "  ax1, ax2 = axes[0]\n",
        "\n",
        "  ax1.axis('off')\n",
        "  ax1.imshow(image)\n",
        "  if correct_flowers_label is not None:\n",
        "    ax1.set_title(tf_flowers_labels[correct_flowers_label])\n",
        "  classes = []\n",
        "  scores = []\n",
        "  logits_max = np.max(logits)\n",
        "  softmax_denominator = np.sum(np.exp(logits - logits_max))\n",
        "  for index, j in enumerate(np.argsort(logits)[-MAX_PREDS::][::-1]):\n",
        "    score = 1.0/(1.0 + np.exp(-logits[j]))\n",
        "    if score < MIN_SCORE: break\n",
        "    if not tf_flowers_logits:\n",
        "      # predicting in imagenet label space\n",
        "      classes.append(imagenet_int_to_str[j])\n",
        "    else:\n",
        "      # predicting in tf_flowers label space\n",
        "      classes.append(tf_flowers_labels[j])\n",
        "    scores.append(np.exp(logits[j] - logits_max)/softmax_denominator*100)\n",
        "\n",
        "  ax2.barh(np.arange(len(scores)) + 0.1, scores)\n",
        "  ax2.set_xlim(0, 100)\n",
        "  ax2.set_yticks(np.arange(len(scores)))\n",
        "  ax2.yaxis.set_ticks_position('right')\n",
        "  ax2.set_yticklabels(classes, rotation=0, fontsize=14)\n",
        "  ax2.invert_xaxis()\n",
        "  ax2.invert_yaxis()\n",
        "  ax2.set_xlabel('Prediction probabilities', fontsize=11)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJU2ecwvqiQQ",
        "colab_type": "text"
      },
      "source": [
        "**TODO: try replacing the URL below with a link to an image of your choice!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVZtHTuR771k",
        "colab_type": "code",
        "outputId": "788c2b98-72bb-4853-c58f-465c88b72625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# Load image (image provided is CC0 licensed)\n",
        "img_url = \"https://p0.pikrepo.com/preview/853/907/close-up-photo-of-gray-elephant.jpg\"\n",
        "image = load_image_from_url(img_url)\n",
        "\n",
        "# Run model on image\n",
        "logits = imagenet_module(image)\n",
        "\n",
        "# Show image and predictions\n",
        "show_preds(logits, image[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viPVvoFOyPr8",
        "colab_type": "text"
      },
      "source": [
        "Here the model correctly classifies the photo as an elephant. It is likely an Asian elephant because of the size of its ears."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRDwXBLzFYFU",
        "colab_type": "text"
      },
      "source": [
        "We will also try predicting on an image from the dataset we're going to fine-tune on - [TF flowers](https://www.tensorflow.org/datasets/catalog/tf_flowers), which has also been used in [other tutorials](https://www.tensorflow.org/tutorials/load_data/images). This dataset contains 3670 images of 5 classes of flowers.\n",
        "\n",
        "Note that the correct label of the image we're going to predict on (‘tulip’) is not a class in ImageNet and so the model cannot predict that at the moment - let’s see what it tries to do instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_KTcxDYhZf_",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "3917e0f7-6112-4143-833a-6da185ae24b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225,
          "referenced_widgets": [
            "77e33a5c8f564620859fe1c4b34ec6a7",
            "d4c00075c69c4ab1bff9dfd08b1ba12c",
            "62d9e468183840fcaf52f2f856a1624c",
            "27cccceb221a4a52b7969759d9e85846",
            "54403b34a464494ab6f9ff95c3584099",
            "516b20f5d8aa455297d84298efb9b741",
            "2932e152f899402a80125a6eeaa8bac6",
            "fda175b0297e4ded87741cc82b9fb395"
          ]
        }
      },
      "source": [
        "# Import tf_flowers data from tfds\n",
        "\n",
        "dataset_name = 'tf_flowers'\n",
        "ds, info = tfds.load(name=dataset_name, split=['train'], in_memory=False, with_info=True)\n",
        "ds = ds[0]\n",
        "num_examples = info.splits['train'].num_examples\n",
        "NUM_CLASSES = 5"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Dataset tf_flowers is hosted on GCS. It will automatically be downloaded to your\n",
            "local data directory. If you'd instead prefer to read directly from our public\n",
            "GCS bucket (recommended if you're running on GCP), you can instead set\n",
            "data_dir=gs://tfds-data/datasets.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset tf_flowers/3.0.0 (download: 218.21 MiB, generated: Unknown size, total: 218.21 MiB) to /root/tensorflow_datasets/tf_flowers/3.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "77e33a5c8f564620859fe1c4b34ec6a7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Dl Completed...', max=5.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\u001b[1mDataset tf_flowers downloaded and prepared to /root/tensorflow_datasets/tf_flowers/3.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ol5TesnUn7bg",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "7fb75a3d-a80c-4f50-d881-196cdf8d9ca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "#@title Alternative code for loading a dataset\n",
        "#@markdown We provide alternative code for loading `tf_flowers` from an URL in this cell to make it easy for you to try loading your own datasets. \n",
        "\n",
        "#@markdown This code is commented out by default and replaces the cell immediately above. Note that using this option may result in a different example image below.\n",
        "\"\"\"\n",
        "data_dir = tf.keras.utils.get_file(origin='https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',\n",
        "                                         fname='flower_photos', untar=True)\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "\n",
        "IMG_HEIGHT = 224\n",
        "IMG_WIDTH = 224\n",
        "\n",
        "CLASS_NAMES = tf_flowers_labels  # from plotting helper functions above\n",
        "NUM_CLASSES = len(CLASS_NAMES)\n",
        "num_examples = len(list(data_dir.glob('*/*.jpg')))\n",
        "\n",
        "def get_label(file_path):\n",
        "  # convert the path to a list of path components\n",
        "  parts = tf.strings.split(file_path, os.path.sep)\n",
        "  # The second to last is the class-directory\n",
        "  \n",
        "  return tf.where(parts[-2] == CLASS_NAMES)[0][0]\n",
        "\n",
        "def decode_img(img):\n",
        "  # convert the compressed string to a 3D uint8 tensor\n",
        "  img = tf.image.decode_jpeg(img, channels=3)\n",
        "  return img  \n",
        "\n",
        "def process_path(file_path):\n",
        "  label = get_label(file_path)\n",
        "  # load the raw data from the file as a string\n",
        "  img = tf.io.read_file(file_path)\n",
        "  img = decode_img(img)\n",
        "  features = {'image': img, 'label': label}\n",
        "  return features\n",
        "\n",
        "list_ds = tf.data.Dataset.list_files(str(data_dir/'*/*'))\n",
        "ds = list_ds.map(process_path, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\ndata_dir = tf.keras.utils.get_file(origin='https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',\\n                                         fname='flower_photos', untar=True)\\ndata_dir = pathlib.Path(data_dir)\\n\\nIMG_HEIGHT = 224\\nIMG_WIDTH = 224\\n\\nCLASS_NAMES = tf_flowers_labels  # from plotting helper functions above\\nNUM_CLASSES = len(CLASS_NAMES)\\nnum_examples = len(list(data_dir.glob('*/*.jpg')))\\n\\ndef get_label(file_path):\\n  # convert the path to a list of path components\\n  parts = tf.strings.split(file_path, os.path.sep)\\n  # The second to last is the class-directory\\n  \\n  return tf.where(parts[-2] == CLASS_NAMES)[0][0]\\n\\ndef decode_img(img):\\n  # convert the compressed string to a 3D uint8 tensor\\n  img = tf.image.decode_jpeg(img, channels=3)\\n  return img  \\n\\ndef process_path(file_path):\\n  label = get_label(file_path)\\n  # load the raw data from the file as a string\\n  img = tf.io.read_file(file_path)\\n  img = decode_img(img)\\n  features = {'image': img, 'label': label}\\n  return features\\n\\nlist_ds = tf.data.Dataset.list_files(str(data_dir/'*/*'))\\nds = list_ds.map(process_path, num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIJyrvKX223E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split into train and test sets\n",
        "# We have checked that the classes are reasonably balanced.\n",
        "train_split = 0.9\n",
        "num_train = int(train_split * num_examples)\n",
        "ds_train = ds.take(num_train)\n",
        "ds_test = ds.skip(num_train)\n",
        "\n",
        "DATASET_NUM_TRAIN_EXAMPLES = num_examples"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3VbdEmog06U",
        "colab_type": "code",
        "outputId": "cd031be0-4998-4466-b7d3-d3096d4b1255",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "for features in ds_train.take(1):\n",
        "  image = features['image']\n",
        "  image = preprocess_image(image)\n",
        "\n",
        "  # Run model on image\n",
        "  logits = imagenet_module(image)\n",
        "  \n",
        "  # Show image and predictions\n",
        "  show_preds(logits, image[0], correct_flowers_label=features['label'].numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9futEJDOfzE",
        "colab_type": "text"
      },
      "source": [
        "In this case, In this case, 'tulip' is not a class in ImageNet, and the model predicts a reasonably similar-looking classe, 'bell pepper'."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXZXPPVShbb9",
        "colab_type": "text"
      },
      "source": [
        "## Fine-tuning the BiT model\n",
        "\n",
        "Now we are going to fine-tune the BiT model so it performs better on a specific dataset. Here we are going to use Keras for simplicity and we are going to fine-tune the model on a dataset of flowers (`tf_flowers`). \n",
        "\n",
        "We will use the model we loaded at the start (i.e. the one not fine-tuned on ImageNet) so the model is less biased towards ImageNet-like images.\n",
        "\n",
        "There are two steps:\n",
        "1. Create a new model with a new final layer (which we call the ‘head’), and\n",
        "2. Fine-tune this model using BiT-HyperRule, our hyperparameter heuristic.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRQcqdj7uIRo",
        "colab_type": "text"
      },
      "source": [
        "### 1. Creating the new model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H22_7mnyt-kY",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "To create the new model, we:\n",
        "\n",
        "1. Cut off the BiT model’s original head. This leaves us with the “pre-logits” output.\n",
        " - We do not have to do this if we use the ‘feature extractor’ models ((i.e. all those in subdirectories titled `feature_vectors`), since for those models the head has already been cut off.\n",
        "\n",
        "2. Add a new head with the number of outputs equal to the number of classes of our new task. Note that it is important that we initialise the head to all zeroes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2pc8tmtcdgZ",
        "colab_type": "text"
      },
      "source": [
        "#### Add new head to the BiT model\n",
        "\n",
        "Since we want to use BiT on a new dataset (not the one it was trained on), we need to replace the final layer with one that has the correct number of output classes. This final layer is called the head.\n",
        "\n",
        "Note that it is important to **initialise the new head to all zeros**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2RGgy4ghgsh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add new head to the BiT model\n",
        "\n",
        "class MyBiTModel(tf.keras.Model):\n",
        "  \"\"\"BiT with a new head.\"\"\"\n",
        "\n",
        "  def __init__(self, num_classes, module):\n",
        "    super().__init__()\n",
        "\n",
        "    self.num_classes = num_classes\n",
        "    self.head = tf.keras.layers.Dense(num_classes, kernel_initializer='zeros')\n",
        "    self.bit_model = module\n",
        "  \n",
        "  def call(self, images):\n",
        "    # No need to cut head off since we are using feature extractor model\n",
        "    bit_embedding = self.bit_model(images)\n",
        "    return self.head(bit_embedding)\n",
        "\n",
        "model = MyBiTModel(num_classes=NUM_CLASSES, module=module)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEYhH53WdmjJ",
        "colab_type": "text"
      },
      "source": [
        "### Data and preprocessing\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqNQ01J0u4xs",
        "colab_type": "text"
      },
      "source": [
        "#### BiT Hyper-Rule: Our hyperparameter selection heuristic\n",
        "When we fine-tune the model, we use BiT-HyperRule, our heuristic for choosing hyperparameters for downstream fine-tuning. This is **not a hyperparameter sweep** - given a dataset, it specifies one set of hyperparameters that we’ve seen produce good results. You can often obtain better results by running a more expensive hyperparameter sweep, but BiT-HyperRule is an effective way of getting good initial results on your dataset.\n",
        "\n",
        "**Hyperparameter heuristic details**\n",
        "\n",
        "In BiT-HyperRule, we use a vanilla SGD optimiser with an initial learning rate of 0.003, momentum 0.9 and batch size 512. We decay the learning rate by a factor of 10 at 30%, 60% and 90% of the training steps. \n",
        "\n",
        "As data preprocessing, we resize the image, take a random crop, and then do a random horizontal flip (details in table below). We do random crops and horizontal flips for all tasks except those where such actions destroy label semantics. E.g. we don’t apply random crops to counting tasks, or random horizontal flip to tasks where we’re meant to predict the orientation of an object.\n",
        "\n",
        "\n",
        "Image area | Resize to | Take random crop of size\n",
        "--- | --- | ---\n",
        "Smaller than 96 x 96 px | 160 x 160 px | 128 x 128 px\n",
        "At least 96 x 96 px | 512 x 512 px | 480 x 480 px\n",
        "\n",
        "*Table 1: Downstream resizing and random cropping details. If images are larger, we resize them to a larger fixed size to take advantage of benefits from fine-tuning on higher resolution.*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lamKHdpM4KOk",
        "colab_type": "text"
      },
      "source": [
        "We also use MixUp for datasets with more than 20k examples. Since the dataset used in this tutorial does not use MixUp, for simplicity and speed, we do not include it in this colab, but include it in our [github repo implementation](https://github.com/google-research/big_transfer)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jp5APPRlheCC",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Set dataset-dependent hyperparameters\n",
        "\n",
        "#@markdown Here we set dataset-dependent hyperparameters. For example, our dataset of flowers has 3670 images of varying size (a few hundred x a few hundred pixels), so the image size is larger than 96x96 and the dataset size is <20k examples. However, for speed reasons (since this is a tutorial and we are training on a single GPU), we will select the `<96x96 px` option and train on lower resolution images. As we will see, we can still attain strong results.\n",
        "\n",
        "#@markdown **Algorithm details: how are the hyperparameters dataset-dependent?** \n",
        "\n",
        "#@markdown It's quite intuitive - we resize images to a smaller fixed size if they are smaller than 96 x 96px and to a larger fixed size otherwise. The number of steps we fine-tune for is larger for larger datasets. \n",
        "\n",
        "IMAGE_SIZE = \"=\\u003C96x96 px\" #@param [\"=<96x96 px\",\"> 96 x 96 px\"]\n",
        "DATASET_SIZE = \"\\u003C20k examples\" #@param [\"<20k examples\", \"20k-500k examples\", \">500k examples\"]\n",
        "\n",
        "if IMAGE_SIZE == \"=<96x96 px\":\n",
        "  RESIZE_TO = 160\n",
        "  CROP_TO = 128\n",
        "else:\n",
        "  RESIZE_TO = 512\n",
        "  CROP_TO = 480\n",
        "\n",
        "if DATASET_SIZE == \"<20k examples\":\n",
        "  SCHEDULE_LENGTH = 500\n",
        "  SCHEDULE_BOUNDARIES = [200, 300, 400]\n",
        "elif DATASET_SIZE == \"20k-500k examples\":\n",
        "  SCHEDULE_LENGTH = 10000\n",
        "  SCHEDULE_BOUNDARIES = [3000, 6000, 9000]\n",
        "else:\n",
        "  SCHEDULE_LENGTH = 20000\n",
        "  SCHEDULE_BOUNDARIES = [6000, 12000, 18000]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bJVjSZuVCCBr",
        "colab_type": "text"
      },
      "source": [
        "**Tip**: if you are running out of memory, decrease the batch size. A way to adjust relevant parameters is to linearly scale the schedule length and learning rate.\n",
        "\n",
        "```\n",
        "SCHEDULE_LENGTH = SCHEDULE_LENGTH * 512 / BATCH_SIZE\n",
        "lr = 0.003 * BATCH_SIZE / 512\n",
        "```\n",
        "\n",
        "These adjustments have already been coded in the cells below - you only have to change the `BATCH_SIZE`. If you change the batch size, please re-run the cell above as well to make sure the `SCHEDULE_LENGTH` you are starting from is correct as opposed to already altered from a previous run."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DiIrQFBhe9R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Preprocessing helper functions\n",
        "\n",
        "# Create data pipelines for training and testing:\n",
        "BATCH_SIZE = 512\n",
        "SCHEDULE_LENGTH = SCHEDULE_LENGTH * 512 / BATCH_SIZE\n",
        "\n",
        "STEPS_PER_EPOCH = 10\n",
        "\n",
        "def cast_to_tuple(features):\n",
        "  return (features['image'], features['label'])\n",
        "  \n",
        "def preprocess_train(features):\n",
        "  # Apply random crops and horizontal flips for all tasks \n",
        "  # except those for which cropping or flipping destroys the label semantics\n",
        "  # (e.g. predict orientation of an object)\n",
        "  features['image'] = tf.image.random_flip_left_right(features['image'])\n",
        "  features['image'] = tf.image.resize(features['image'], [RESIZE_TO, RESIZE_TO])\n",
        "  features['image'] = tf.image.random_crop(features['image'], [CROP_TO, CROP_TO, 3])\n",
        "  features['image'] = tf.cast(features['image'], tf.float32) / 255.0\n",
        "  return features\n",
        "\n",
        "def preprocess_test(features):\n",
        "  features['image'] = tf.image.resize(features['image'], [RESIZE_TO, RESIZE_TO])\n",
        "  features['image'] = tf.cast(features['image'], tf.float32) / 255.0\n",
        "  return features\n",
        "\n",
        "pipeline_train = (ds_train\n",
        "                  .shuffle(10000)\n",
        "                  .repeat(int(SCHEDULE_LENGTH * BATCH_SIZE / DATASET_NUM_TRAIN_EXAMPLES * STEPS_PER_EPOCH) + 1 + 50)  # repeat dataset_size / num_steps\n",
        "                  .map(preprocess_train, num_parallel_calls=8)\n",
        "                  .batch(BATCH_SIZE)\n",
        "                  .map(cast_to_tuple)  # for keras model.fit\n",
        "                  .prefetch(2))\n",
        "\n",
        "pipeline_test = (ds_test.map(preprocess_test, num_parallel_calls=1)\n",
        "                  .map(cast_to_tuple)  # for keras model.fit\n",
        "                  .batch(BATCH_SIZE)\n",
        "                  .prefetch(2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xU1SoL32eD4f",
        "colab_type": "text"
      },
      "source": [
        "### Fine-tuning loop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmckWR7wz2d2",
        "colab_type": "text"
      },
      "source": [
        "The fine-tuning will take about 15 minutes. If you wish, you can manually set the number of epochs to be 10 instead of 50 for the tutorial, and you will likely still obtain a model with validation accuracy > 99%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zx60lxfzhoTQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define optimiser and loss\n",
        "\n",
        "lr = 0.003 * BATCH_SIZE / 512 \n",
        "\n",
        "# Decay learning rate by a factor of 10 at SCHEDULE_BOUNDARIES.\n",
        "lr_schedule = tf.keras.optimizers.schedules.PiecewiseConstantDecay(boundaries=SCHEDULE_BOUNDARIES, \n",
        "                                                                   values=[lr, lr*0.1, lr*0.001, lr*0.0001])\n",
        "optimizer = tf.keras.optimizers.SGD(learning_rate=lr_schedule, momentum=0.9)\n",
        "\n",
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJmqsAFmeQZd",
        "colab_type": "code",
        "outputId": "9859d60d-0a45-47ba-a54b-350667abd2f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.compile(optimizer=optimizer,\n",
        "              loss=loss_fn,\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Fine-tune model\n",
        "history = model.fit(\n",
        "    pipeline_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    steps_per_epoch=STEPS_PER_EPOCH,\n",
        "    epochs= int(SCHEDULE_LENGTH / STEPS_PER_EPOCH),  # TODO: replace with `epochs=10` here to shorten fine-tuning for tutorial if you wish\n",
        "    validation_data=pipeline_test  # here we are only using \n",
        "                                   # this data to evaluate our performance\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.3932 - accuracy: 0.8529 - val_loss: 0.1047 - val_accuracy: 0.9809\n",
            "Epoch 2/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.1674 - accuracy: 0.9535 - val_loss: 0.1042 - val_accuracy: 0.9837\n",
            "Epoch 3/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.1413 - accuracy: 0.9586 - val_loss: 0.0992 - val_accuracy: 0.9782\n",
            "Epoch 4/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.1103 - accuracy: 0.9707 - val_loss: 0.0997 - val_accuracy: 0.9809\n",
            "Epoch 5/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.0908 - accuracy: 0.9693 - val_loss: 0.0958 - val_accuracy: 0.9837\n",
            "Epoch 6/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0785 - accuracy: 0.9748 - val_loss: 0.0922 - val_accuracy: 0.9837\n",
            "Epoch 7/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.0685 - accuracy: 0.9771 - val_loss: 0.0896 - val_accuracy: 0.9837\n",
            "Epoch 8/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.0639 - accuracy: 0.9793 - val_loss: 0.0852 - val_accuracy: 0.9782\n",
            "Epoch 9/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.0491 - accuracy: 0.9836 - val_loss: 0.0755 - val_accuracy: 0.9809\n",
            "Epoch 10/50\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.0434 - accuracy: 0.9861 - val_loss: 0.0765 - val_accuracy: 0.9809\n",
            "Epoch 11/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0417 - accuracy: 0.9879 - val_loss: 0.0773 - val_accuracy: 0.9755\n",
            "Epoch 12/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0363 - accuracy: 0.9898 - val_loss: 0.0769 - val_accuracy: 0.9755\n",
            "Epoch 13/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0379 - accuracy: 0.9883 - val_loss: 0.0746 - val_accuracy: 0.9782\n",
            "Epoch 14/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0339 - accuracy: 0.9900 - val_loss: 0.0797 - val_accuracy: 0.9782\n",
            "Epoch 15/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0318 - accuracy: 0.9891 - val_loss: 0.0729 - val_accuracy: 0.9809\n",
            "Epoch 16/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0282 - accuracy: 0.9920 - val_loss: 0.0737 - val_accuracy: 0.9782\n",
            "Epoch 17/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0279 - accuracy: 0.9932 - val_loss: 0.0751 - val_accuracy: 0.9782\n",
            "Epoch 18/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0288 - accuracy: 0.9924 - val_loss: 0.0778 - val_accuracy: 0.9782\n",
            "Epoch 19/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0256 - accuracy: 0.9926 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 20/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0256 - accuracy: 0.9924 - val_loss: 0.0721 - val_accuracy: 0.9837\n",
            "Epoch 21/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0253 - accuracy: 0.9928 - val_loss: 0.0725 - val_accuracy: 0.9809\n",
            "Epoch 22/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0199 - accuracy: 0.9939 - val_loss: 0.0722 - val_accuracy: 0.9837\n",
            "Epoch 23/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0199 - accuracy: 0.9959 - val_loss: 0.0734 - val_accuracy: 0.9809\n",
            "Epoch 24/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0289 - accuracy: 0.9918 - val_loss: 0.0735 - val_accuracy: 0.9809\n",
            "Epoch 25/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0219 - accuracy: 0.9928 - val_loss: 0.0731 - val_accuracy: 0.9809\n",
            "Epoch 26/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0228 - accuracy: 0.9939 - val_loss: 0.0728 - val_accuracy: 0.9837\n",
            "Epoch 27/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0220 - accuracy: 0.9947 - val_loss: 0.0728 - val_accuracy: 0.9809\n",
            "Epoch 28/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0209 - accuracy: 0.9941 - val_loss: 0.0733 - val_accuracy: 0.9809\n",
            "Epoch 29/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0229 - accuracy: 0.9939 - val_loss: 0.0734 - val_accuracy: 0.9809\n",
            "Epoch 30/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0183 - accuracy: 0.9959 - val_loss: 0.0736 - val_accuracy: 0.9809\n",
            "Epoch 31/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0219 - accuracy: 0.9937 - val_loss: 0.0734 - val_accuracy: 0.9809\n",
            "Epoch 32/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0230 - accuracy: 0.9939 - val_loss: 0.0733 - val_accuracy: 0.9809\n",
            "Epoch 33/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0212 - accuracy: 0.9937 - val_loss: 0.0733 - val_accuracy: 0.9809\n",
            "Epoch 34/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0193 - accuracy: 0.9951 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 35/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0209 - accuracy: 0.9949 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 36/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0210 - accuracy: 0.9937 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 37/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0202 - accuracy: 0.9947 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 38/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0179 - accuracy: 0.9969 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 39/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0199 - accuracy: 0.9949 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 40/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0233 - accuracy: 0.9937 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 41/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0225 - accuracy: 0.9934 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 42/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0240 - accuracy: 0.9936 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 43/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0230 - accuracy: 0.9936 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 44/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0205 - accuracy: 0.9941 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 45/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0180 - accuracy: 0.9957 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 46/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0223 - accuracy: 0.9939 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 47/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 48/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0228 - accuracy: 0.9941 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 49/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0219 - accuracy: 0.9947 - val_loss: 0.0732 - val_accuracy: 0.9809\n",
            "Epoch 50/50\n",
            "10/10 [==============================] - 13s 1s/step - loss: 0.0238 - accuracy: 0.9932 - val_loss: 0.0732 - val_accuracy: 0.9809\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8p4YCZnivV7K",
        "colab_type": "text"
      },
      "source": [
        "We see that our model attains over 98-99% training and validation accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0ahkv3ZI0Wa",
        "colab_type": "text"
      },
      "source": [
        "## Save fine-tuned model for later use\n",
        "\n",
        "It is easy to save your model to use later on. You can then load your saved model in exactly the same way as we loaded the BiT models at the start.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOYOLzWblDSf",
        "colab_type": "code",
        "outputId": "7740b6f2-70bf-4ddd-c77d-9d977fdca559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "# Save fine-tuned model as SavedModel\n",
        "export_module_dir = '/tmp/my_saved_bit_model/'\n",
        "tf.saved_model.save(model, export_module_dir)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/my_saved_bit_model/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/my_saved_bit_model/assets\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlgQVjWvlf_6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load saved model\n",
        "saved_module = hub.KerasLayer(export_module_dir, trainable=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMKx3A0Z0h6E",
        "colab_type": "code",
        "outputId": "9c92c06b-0194-4d66-c893-4d30ef67fd47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# Visualise predictions from new model\n",
        "for features in ds_train.take(1):\n",
        "  image = features['image']\n",
        "  image = preprocess_image(image)\n",
        "  image = tf.image.resize(image, [CROP_TO, CROP_TO])\n",
        "\n",
        "  # Run model on image\n",
        "  logits = saved_module(image)\n",
        "  \n",
        "  # Show image and predictions\n",
        "  show_preds(logits, image[0], correct_flowers_label=features['label'].numpy(), tf_flowers_logits=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0voqBidA_Wc",
        "colab_type": "text"
      },
      "source": [
        "Voila - we now have a model that predicts tulips as tulips and not bell peppers. :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_y12tl_vj9m",
        "colab_type": "text"
      },
      "source": [
        "## Summary\n",
        "\n",
        "In this post, you learned about the key components we used to train models that can transfer well to many different tasks. You also learned how to load one of our BiT models, fine-tune it on your target task and save the resulting model. Hope this helped and happy fine-tuning!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLUVIYWG10yX",
        "colab_type": "text"
      },
      "source": [
        "## Acknowledgements\n",
        "\n",
        "This colab is based on work by Alex Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly and Neil Houlsby. We thank many members of Brain Research Zurich and the TensorFlow team for their feedback, especially Luiz Gustavo Martins and Marcin Michalski.\n"
      ]
    }
  ]
}